A Large-Scale Empirical Study on Improving the Fairness of Image Classification Models-Reference-Cited by-同舟云学术

A Large-Scale Empirical Study on Improving the Fairness of Image Classification Models

Published:2024-09-11 Issue: Volume:69 Page:210-222
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Container-title:Proceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis
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Author:

Yang Junjie¹^ORCID,Jiang Jiajun¹^ORCID,Sun Zeyu²^ORCID,Chen Junjie¹^ORCID

Affiliation:

1. Tianjin University, Tianjin, China

2. Institute of Software at Chinese Academy of Sciences, Beijing, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3650212.3652122

Reference98 articles.

1. Alekh Agarwal, Alina Beygelzimer, Miroslav Dudík, John Langford, and Hanna M. Wallach. 2018. A Reductions Approach to Fair Classification. In ICML 2018, Jennifer G. Dy and Andreas Krause (Eds.) (Proceedings of Machine Learning Research, Vol. 80). PMLR, 60–69. http://proceedings.mlr.press/v80/agarwal18a.html

2. Aniya Aggarwal, Pranay Lohia, Seema Nagar, Kuntal Dey, and Diptikalyan Saha. 2019. Black box fairness testing of machine learning models. In ESEC/FSE 2019, Marlon Dumas, Dietmar Pfahl, Sven Apel, and Alessandra Russo (Eds.). ACM, 625–635.

3. Jing An, Lexing Ying, and Yuhua Zhu. 2021. Why resampling outperforms reweighting for correcting sampling bias with stochastic gradients. In ICLR 2021. OpenReview.net.

4. Sumon Biswas and Hridesh Rajan. 2020. Do the machine learning models on a crowd sourced platform exhibit bias? an empirical study on model fairness. In ESEC/FSE 2020, Prem Devanbu, Myra B. Cohen, and Thomas Zimmermann (Eds.). ACM, 642–653.

5. Fair preprocessing: towards understanding compositional fairness of data transformers in machine learning pipeline